It is estimated that 80% to 90% of the data worldwide is unstructured. However, when we look for data in a specific domain or organization, we often end up finding structured data. The most likely reason is that structured data is still the de facto standard for quantitative information. Consequently, in the age of Large ..read more

When I first delved into machine learning, prompt engineering seemed like a niche area, outside of the scope of what an engineer like me needed to know. Yet, as large language models (LLMs) have evolved, it has become clear that prompt engineering is not only a skill but a critical component in the LLMOps value ..read more

Text summarization is a prime use case of LLMs (Large Language Models). It aims to condense large amounts of complex information into a shorter, more understandable version, enabling users to review more materials in less time and make more informed decisions. Despite being widely applied in sectors such as journalism, research, and business intelligence, evaluating ..read more

Observability is invaluable in LLMOps. Whether we’re talking about pretraining or agentic networks, it’s paramount that we understand what’s going on inside our systems to control, optimize, and evolve them. The infrastructure, effort, and scale required to achieve observability vary significantly. I recently gave a talk about this topic at the AI Engineer World’s Fair ..read more

Large Language Models (LLMs) have become the driving force behind AI-powered applications, ranging from translation services to chatbots and RAG systems. Along with these applications, a new tech stack has emerged. Beyond LLMs, it comprises components such as vector databases and orchestration frameworks. Developers apply architectural patterns like chains and agents to create powerful applications ..read more

As machine learning (ML) drives innovation across industries, organizations seek ways to improve and optimize their ML workflows. End-to-end (E2E) MLOps platforms promise to simplify the complicated process of building, deploying, and maintaining ML models in production. However, while E2E MLOps platforms promise convenience and integration, they may not always align with an organization’s specific needs, existing infrastructure, or long-term goals. In some cases, assembling a custom MLOps stack using individual components may provide greater flexibility, control, and cost-effectiveness. To he ..read more

TL;DR Adversarial attacks manipulate ML model predictions, steal models, or extract data. Different attack types exist, including evasion, data poisoning, Byzantine, and model extraction attacks. Defense strategies like adversarial learning, monitoring, defensive distillation, and differential privacy improve robustness against adversarial attacks. Multiple aspects have to be considered when evaluating the effectiveness of different defense strategies, including the method’s robustness, impact on model performance, and adaptability to the constant flow of brand-new attack mechan ..read more

TL;DR MLflow proved to have many limitations that neptune.ai can address, providing better security, more robust collaboration tools, and a user-friendly interface. The migration is not as complex as you might think. neptune.ai developed solutions to ease this process. Your MLflow run logs can easily be exported to the neptune.ai app using a dedicated plugin. Use our MLflow vs neptune.ai API comparison table to migrate your training scripts faster. As an MLflow user, it is straightforward to adapt to neptune.ai’s UI. MLflow is a framework widely used for its experiment-tr ..read more

TL;DR Building a great AI system takes more than creating one good model. Instead, you have to implement a workflow that enables you to iterate and continuously improve. Data scientists often lack focus, time, or knowledge about software engineering principles. As a result, poor code quality and reliance on manual workflows are two of the main issues in ML development processes. Using the following three principles helps you build a mature ML development process: Establish a standard repository structure you can use as a scaffold for your projects. Design your scripts, jobs ..read more

TL;DR Using CI/CD workflows to run ML experiments ensures their reproducibility, as all the required information has to be contained under version control. GitHub’s CI/CD solution, GitHub Actions, is popular because it’s directly integrated into the platform and easy to use. GitHub Actions and Neptune are an ideal combination for automating machine-learning model training and experimentation. Getting started with CI/CD for experiment management requires just a few changes to the training code, ensuring that it can run standalone on a remote machine. The compute resources offered ..read more